Individuals often wish to visit locations, such as commercial establishments, based upon the expected demographic composition of the individuals that frequent such locations. For example, young adults intending to visit a restaurant or bar often obtain recommendations from colleagues or perform searches online to obtain information about one or more restaurant or bar locations and base their decision on whether to visit a given restaurant or bar on, at least in part, the attributes of the individuals that frequent such establishments. Such attributes may include, but are not limited to, for example, the average age, sex, relationship status, hobbies, income level, race, national origin or education level of such individuals.
For example, a user using a search engine may enter a query comprising one or more search terms describing a particular location, such as a restaurant. In response to the query, one or more results may be returned. Included in such results may be one or more reviews of the restaurant, which contain, among other information, descriptions of the attributes of the individuals that frequent such restaurants. For example, one or more reviews may indicate that a given restaurant is often frequented on Saturday evenings by “college students” or “young adults.” Similarly, the one or more reviews may indicate that a restaurant is often frequented during weekday lunch hours by “middle-aged businessmen.” Alternatively, or in conjunction with the foregoing, a user may request information from friends, colleagues, family, etc., regarding a given location, such as a restaurant. Such friends, colleagues, family, etc., may provide similar information to the user, which the user may utilize in making a decision on whether or not to visit the restaurant, and if so, a time at which to visit the restaurant based upon such information.
Existing techniques for obtaining information regarding a given location, however, do not allow a user to obtain live, current or near-current information regarding locations and the attributes of the one or more individuals at such locations. Rather, existing techniques require a user to make a decision on whether to visit a given location based upon historical data, such as data written in online reviews, information obtained from colleagues and friends, etc. Although techniques exist that allow users to, for example, “check-in” to locations, such information is often stale, requires that individuals at such locations take affirmative steps to make it known of their whereabouts, and/or is only available to a limited set of individuals, which often comprises a preselected set, such as users' friends. Further, such techniques fail to provide information regarding the demographic composition of a location and instead, simply identify the location of one or more individuals if such users elect to identify their locations.
Accordingly, there is a need for systems and methods to provide information that is live, current or near-current regarding the demographic composition of the one or more individuals at one or more locations to thereby allow potential visitors, patrons, marketers, etc., to make an informed decision on whether or not to visit such one or more locations based on such individuals' attributes. Further, there is a need for systems and methods to identify one or more locations at which one or more individuals with desired attributes are currently present to assist users in determining whether to visit such locations or to select a location that has the greatest number of individuals with one or more desired attributes.